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Fabien Plisson

@fabienplisson.bsky.social

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Fabien Plisson @fabienplisson.bsky.social · Nov 10

Happy to launch Iɴɢᴇɴɪᴇ Bɪᴏ, a consulting firm offering data-driven solutions for biomolecular discovery and design! We specialize in AI-driven molecular design, computational chemistry, and more. Let's collaborate! Visit ingeniebio.com #AI #ML #drugdiscovery

Ingenie Bio | biomolecular design

Ingenie Bio integrates data-driven solutions to support biomolecular design across drug discovery and beyond.

ingeniebio.com

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Reposted by Fabien Plisson

Drew Jolly @amjolly.bsky.social · Aug 8

NSF Invests Nearly $32M to Accelerate Novel AI-Driven Approaches in Protein Design
ow.ly/NJzp50WCc00 #NSF #AIwire

NSF Invests Nearly $32M to Accelerate Novel AI-Driven Approaches in Protein Design

Aug. 8, 2025 -- The U.S. National Science Foundation Directorate for Technology, Innovation and Partnerships (NSF TIP) announced an inaugural investment

ow.ly

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Reposted by Fabien Plisson

Keith Fitzgerald @keithfitzgerald.bsky.social · Jun 26

Worth a watch:

Head of Signal, Meredith Whittaker, on so-called "agentic AI" and the difference between how it's described in the marketing and what access and control it would actually require to work as advertised.

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Fabien Plisson @fabienplisson.bsky.social · Jun 28

If that helps, we compared AF2 predicted structures of a small set of peptides against their PDB structures under different experimental methods (NMR solutions, X-ray, SSNMR, EM) as ground truth. pubs.rsc.org/en/content/a...

Benchmarking protein structure predictors to assist machine learning-guided peptide discovery

Machine learning models provide an informed and efficient strategy to create novel peptide and protein sequences with the desired profiles. Nevertheless, they are primarily trained on sequences where ...

pubs.rsc.org

Reposted by Fabien Plisson

Daniel Hurdiss @danielhurdiss.bsky.social · May 23

Think coronavirus spikes have run out of surprises? Think again.

Our latest preprint dives into the highly unusual spikes of marine mammal coronaviruses.

www.biorxiv.org/content/10.1...

This #cryoEM study was led by @viralfusion.bsky.social, with key contributions from an amazing team.

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Fabien Plisson @fabienplisson.bsky.social · May 5

Derguini, F.; Plisson, F.; Massiot, G. 𝘗𝘳𝘦𝘱𝘢𝘳𝘢𝘵𝘪𝘰𝘯 𝘰𝘧 𝘵𝘢𝘨𝘪𝘵𝘪𝘯𝘪𝘯 𝘊 𝘢𝘯𝘥 𝘍 𝘥𝘦𝘳𝘪𝘷𝘢𝘵𝘪𝘷𝘦𝘴 𝘢𝘴 𝘢𝘯𝘵𝘪-𝘤𝘢𝘯𝘤𝘦𝘳 𝘢𝘨𝘦𝘯𝘵𝘴. FR 2941697 A1 20100806 2010.
Europe PMC europepmc.org/article/PAT/...
Google Patents
patents.google.com/patent/FR294...

Fabien Plisson @fabienplisson.bsky.social · May 5

As an engineer at heart, I wondered how we would scale up if we found a non-toxic, bioactive analogue. That led us to 𝗯𝗶𝗼𝗰𝗮𝘁𝗮𝗹𝘆𝘀𝗶𝘀 (think immobilised enzymes) - screening lipases and esterases to reach, in 1 step, a key intermediate: Taginitol C. This work was patented.

Fabien Plisson @fabienplisson.bsky.social · May 5

Over three years, we synthesised dozens of Taginitin C analogues, eventually streamlining the synthesis from 10 to 6 steps 𝘷𝘪𝘢 judicious protection/deprotection strategies.

Fabien Plisson @fabienplisson.bsky.social · May 5

The molecule was notoriously sensitive to acids, bases, nucleophiles, and UV light. Resources were limited, and we were performing multi-step syntheses on just 𝟱 𝗺𝗴 𝘀𝗰𝗮𝗹𝗲𝘀, in 𝘁𝗶𝗻𝘆 𝗿𝗼𝘂𝗻𝗱-𝗯𝗼𝘁𝘁𝗼𝗺 𝗳𝗹𝗮𝘀𝗸𝘀, working in darkened fume hoods.

Fabien Plisson @fabienplisson.bsky.social · May 5

Together with various teammates (MSc students) we worked on the hit-lead optimisation of Tagitinin C - a toxic germanocrolide inhibiting the ubiquitin-proteasome pathway.

Fabien Plisson @fabienplisson.bsky.social · May 5

Eighteen years ago, I joined one of my first drug development R&D programs at the 𝘐𝘯𝘴𝘵𝘪𝘵𝘶𝘵 𝘥𝘦𝘴 𝘚𝘤𝘪𝘦𝘯𝘤𝘦𝘴 𝘦𝘵 𝘛𝘦𝘤𝘩𝘯𝘰𝘭𝘰𝘨𝘪𝘦𝘴 𝘥𝘶 𝘔𝘦𝘥𝘪𝘤𝘢𝘮𝘦𝘯𝘵 𝘥𝘦 𝘛𝘰𝘶𝘭𝘰𝘶𝘴𝘦 - a public-private partnership between the @cnrs.fr and Pierre Fabre Laboratories.

Fabien Plisson @fabienplisson.bsky.social · May 5

Never too late to publish, I am proud to see this work finally published: Tagitinin C, a Sesquiterpene Lactone, and Derivatives as Proteasome Inhibitors doi.org/10.1002/ejoc...

Tagitinin C, a Sesquiterpene Lactone, and Derivatives as Proteasome Inhibitors

Tagitinin C, a germacranolide, isolated from Tithonia diversifolia was shown to have an interesting level of activity on the proteasome pathway. It is however a particularly unstable molecule, sensit...

doi.org

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Reposted by Fabien Plisson

Colin Jackson @cjjackson.bsky.social · Apr 22

Choosing ML architectures for protein engineering is often challenging. Our “new” updated preprint provides a rational framework to match ML models to protein fitness tasks, showing landscape ruggedness influences prediction accuracy. Mahakaran dana Adam et al www.biorxiv.org/content/10.1...

Investigating the determinants of performance in machine learning for protein fitness prediction

Machine learning (ML) has revolutionized protein biology, solving long-standing problems in protein folding, scaffold generation and function design tasks. A range of architectures have shown success ...

www.biorxiv.org

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Reposted by Fabien Plisson

Martin Steinegger 🇺🇦 @martinsteinegger.bsky.social · Mar 29

Run BioEmu in Colab - just click "Runtime → Run all"! Our notebook uses ColabFold to generate MSAs, BioEmu to predict trajectories, and Foldseek to cluster conformations.
Thanks @jjimenezluna.bsky.social for the help!
🌐 colab.research.google.com/github/sokry...
📄 www.biorxiv.org/content/10.1...

Google Colab

colab.research.google.com

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Reposted by Fabien Plisson

Gina El Nesr @ginaelnesr.bsky.social · Mar 20

Protein function often depends on protein dynamics. To design proteins that function like natural ones, how do we predict their dynamics?

@hkws.bsky.social and I are thrilled to share the first big, experimental datasets on protein dynamics and our new model: Dyna-1!

🧵

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Reposted by Fabien Plisson

Irina Bezsonova @irinabezsonova.bsky.social · Mar 15

You can download my protein structure-inspired artwork from pdb webpage:

pdb101.rcsb.org/sci-art/bezs...

@rcsbpdb.bsky.social
@pdbeurope.bsky.social
#sciart

PDB101: Irina Bezsonova Gallery

PDB-101: Training, Outreach, and Education portal of RCSB PDB

pdb101.rcsb.org

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Reposted by Fabien Plisson

Alicia Cintron, PhD @cintronrevised.com · Feb 19

YouTube is the world's 2nd-largest search engine. So why aren't more conference keynotes and presentations there? 🤔

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Reposted by Fabien Plisson

Carl T. Bergstrom @carlbergstrom.com · Feb 4

Modern-Day Oracles or Bullshit Machines?

Jevin West (@jevinwest.bsky.social) and I have spent the last eight months developing the course on large language models (LLMs) that we think every college freshman needs to take.

thebullshitmachines.com

INTRODUCTION

thebullshitmachines.com

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Reposted by Fabien Plisson

Diego del Alamo @delalamo.xyz · Feb 17

Short thread about this interesting preprint that explores antibody design by combining MD with inverse folding and active learning. It's a bit rough around the edges but it introduces a cool idea I hope is further fleshed out www.biorxiv.org/content/10.1...

Fig. 1 Inverse Folding Molecular Dynamics (IF-MD) protocol. (A) Free energy landscape
of the process of protein-protein dissociation in the case of SARS-CoV-2 RBD (red) in complex with
the nanobody H11 (blue) visiting different metastable states along the unbinding mechanism along
two collective variables. Two different unbinding trajectories from the bound state are shown in black
and red. (B) IF-MD protocol to sample protein sequence space by constraining k ensemble averaged
observables q
target
k
and using Bayesian Optimization to propose new sequences. (C) Specific example
of IF-MD constrained to a target the unbinding rate constant, and using Infrequent Metadynamics
to sample the prior ensemble. Unbinding example trajectories sampled by Infrequent Metadynamics
are shon in blue, along the RMSD from the cryo-EM structure of H11 bound to SARS-CoV-2 RBD
as a function of time.

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Fabien Plisson @fabienplisson.bsky.social · Feb 13

Are protein language models the universal key?
doi.org/10.1016/j.sb...

Brilliant and thoughtful piece.

Redirecting

doi.org

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Fabien Plisson @fabienplisson.bsky.social · Feb 12

Brilliant video on the development of protein structure prediction featuring #AF2 and #Rosetta series
youtu.be/P_fHJIYENdI?...

What if all the world's biggest problems have the same solution?

YouTube video by Veritasium

youtu.be

Reposted by Fabien Plisson

César de la Fuente @delafuentelab.bsky.social · Feb 5

(1/4)In our new paper in @cp-trendsgenetics.bsky.social @cellpress.bsky.social, we highlight some fascinating tiny proteins—microproteins—which appear to be widespread in nature. www.cell.com/trends/genet...

Microproteins: emerging roles as antibiotics

Recent advances in computational prediction and experimental techniques have detected previously unknown microproteins, particularly in the human microbiome. These small proteins, produced by diverse ...

www.cell.com

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Fabien Plisson @fabienplisson.bsky.social · Jan 30

Great editorial from @evotec.bsky.social using AI+ML tools to assist drug discovery campaigns in the small and middle chemical spaces
pubs.acs.org/doi/10.1021/...

Real-World Applications and Experiences of AI/ML Deployment for Drug Discovery

OR SEARCH CITATIONS

pubs.acs.org

Fabien Plisson @fabienplisson.bsky.social · Jan 29

Beilun Wang and team recently reported the development of antimicrobial peptides combining an ensemble of DL predictors with EvoGradient, modifying candidate sequences strategically from evolutionary information, improving their antimicrobial activity and selectivity.
www.nature.com/articles/s41...

Explainable deep learning and virtual evolution identifies antimicrobial peptides with activity against multidrug-resistant human pathogens - Nature Microbiology

An AI-based learning model is applied to low-abundance human oral bacteria and identifies antimicrobial peptides with efficacy against multidrug-resistant bacterial pathogens.

www.nature.com

Fabien Plisson @fabienplisson.bsky.social · Jan 29

We previously used that strategy to set guidelines for discovering and designing non-hemolytic peptides:
www.nature.com/articles/s41...

Machine learning-guided discovery and design of non-hemolytic peptides - Scientific Reports

Scientific Reports - Machine learning-guided discovery and design of non-hemolytic peptides

www.nature.com

Fabien Plisson @fabienplisson.bsky.social · Jan 29

The process mimics the hypothesis-driven process of a medicinal chemist, especially with Deep Mutational Scanning. It also merits explaining the results from your predictive model(s) by comparing mutants.